There is currently significant growth in the number of ML-powered applications. This brings benefits, but it also provides grounds for attackers to exploit unsuspecting ML users. This is why Google launched the Secure AI Framework (SAIF) to establish industry standards for creating trustworthy and responsible AI applications. The first principle of SAIF is to
Expand strong security foundations to the AI ecosystem
Building on the work with Open Source Security Foundation, we are creating this repository to demonstrate how the ML supply chain can be strengthened in the same way as the traditional software supply chain.
This repository hosts a collection of utilities and examples related to the security of machine learning pipelines. The focus is on providing verifiable claims about the integrity and provenance of the resulting models, meaning users can check for themselves that these claims are true rather than having to just trust the model trainer.
Currently, there are two main projects in the repository: model signing (to prevent tampering of models after publication to ML model hubs) and SLSA (to prevent tampering of models during the build process).
This project demonstrates how to protect the integrity of a model by signing it with Sigstore, a tool for making code signatures transparent without requiring management of cryptographic key material.
When users download a given version of a signed model they can check that the signature comes from a known or trusted identity and thus that the model hasn't been tampered with after training.
We are able to sign large models with very good performance, as the following table shows:
| Model | Size | Sign Time | Verify Time |
|---|---|---|---|
| roberta-base-11 | 8K | 1s | 0.6s |
| hustvl/YOLOP | 215M | 1s | 1s |
| bertseq2seq | 2.8G | 1.9s | 1.4s |
| bert-base-uncased | 3.3G | 1.6s | 1.1s |
| tiiuae/falcon-7b | 14GB | 2.1s | 1.8s |
See README.model_signing.md for more information.
This project shows how we can generate SLSA provenance for ML models, using either Github Actions or Google Cloud Platform.
SLSA was originally developed for traditional software to protect against tampering with builds, such as in the Solarwinds attack, and this project is a proof of concept that the same supply chain protections can be applied to ML.
We support both TensorFlow and PyTorch models. The examples train a model on [CIFAR10][cifar10] dataset, save it in one of the supported formats, and generate provenance for the output. The supported formats are:
| Workflow Argument | Training Framework | Model format |
|---|---|---|
tensorflow_model.keras |
TensorFlow | Keras format (default) |
tensorflow_hdf5_model.h5 |
TensorFlow | Legacy HDF5 format |
tensorflow_hdf5.weights.h5 |
TensorFlow | Legacy HDF5 weights only format |
pytorch_model.pth |
PyTorch | PyTorch default format |
pytorch_full_model.pth |
PyTorch | PyTorch complete model format |
pytorch_jitted_model.pt |
PyTorch | PyTorch TorchScript format |
See slsa_for_models/README.md for more information.
This project is currently experimental, not ready for all production use-cases. We may make breaking changes until the first official release.
Please see the Contributor Guide for more information.